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<CENTER><TITLE>NIST Scoring Toolkit Version 2.0beta</TITLE></CENTER>
<B><BIG><CENTER> Welcome to the NIST Scoring Toolkit Version 2.0 Beta 1</CENTER></BIG></B>
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The NIST Scoring Toolkit (SCTK) is a collection of software tools
designed to score benchmark test evaluations of Automatic Speech
Recognition (ASR) Systems.  The toolkit is currently used by NIST,
benchmark test participants, and reserchers worldwide to as a common
scoring engine.

<p>See the "Demo" section below to get a quick intro into key concepts>
    
<P>
<h2> This version of SCTK contains several programs: </h2>
<P>
<B><A HREF="sclite.htm">sclite</A></B>

<UL>
<P>SCTK has at its core, sclite (Score-Lite), which is a flexible
Dynamic Programming alignment engine used to "align" errorful
hypothesized texts, such as output from an ASR system, to the correct
reference texts.  After alignment, sclite generates a veriety of
summary as well as detailed scoring reports.
<P>
This version of sclite comes bundled with the 
<A HREF="../src/slm_v2/doc/toolkit_documentation.html"> CMU-Cambridge Statistical Language
Modeling Toolkit v2.</A>  The toolkit is used to compute word-weights based on an N-gram language
model.  The directory 'src/slm_v2' contains the complete distribution and is automatically
compiled by the installation scripts.

</UL>

<B><A HREF="sc_stats.htm">sc_stats</A></B> <UL> <P>While sclite aligns
and scores a single system, sc_stats will compare system performance
between more than one system, so long as the systems under test have  been
ran on identical test data and using an identical test paradigm.  Inter-System
comparisons are made by running tests paired-comparison statistical
significance tests.  </UL>

<B><A HREF="rover.htm">rover</A></B> <UL> <P>Rover - Recognition
Output Voting Error Reduction, is a tool which combines an arbitrary
number for ASR system outputs into a composite Word Transition network
which is then searched an scored to retrieve the best scoring word
sequence.

<P>The program is documented in the paper <A HREF="rover/rover.htm">
A post-processing system to yield reduced word error rates: Recognizer
Output Voting Error Reduction (ROVER)</A> presented at the 1997 IEEE Workshop
on Automatic Speech Recognition and Understanding.  The paper is also
available in <A HREF="rover/rover.ps"> postscript.</a>

</UL>

<B>hubscr</B> <UL> Wrapper script for scoring DARPA STT evaluations </UL>
<B>rfilter1</B> <UL> Transcription normalization filter engine </UL>
<B>csrfilt </B> <UL> Transcription normalization wrapper script </UL>
<B>chfilt </B> <UL> CallHome/Switchboard style transcript filter </UL>
<B>hamzaNorm </B> <UL> Arabic Hamza+alif transcript normalization filter </UL>
<B>acomp </B> <UL> Automatic compound word expansion filter </UL>
<B>def_art </B> <UL> Aribic definate article striping program </UL>
<B>utf_filt </B> <UL> UTF file format transcription filter </UL>


<H2> ASR Scoring Demo </h2>
  <P> So. what is this package for?  It's for computing the accuracy
  of ASR engines that convert recordings of speech into text. We'll use data in <a href=../src/sclite/testdata>../src/sclite/testdata</a>  for the demo. The
    process to compute the accuracy is:
<OL>
<li> Record a sample of speech (called an utterance)
  storing it in a waveform file like a .wav or .mp3.  Each recording has a unique utterance id used later. 
<lI> Manually transcribe the speech to build what we call the 'reference'
  transcription - some call it the ground truth but the term reference
  is preferred because it is costly/difficult/impossible to make 100%
  accurate transctript.
<li> Convert the audio into the 'hypothesized'  text with a program like
  <A href=https://www.goodfirms.co/software/kaldi> Kaldi </a>.  The text is hypothesized to be correct by the system.
<li> Assemble the reference and hypothsis texts into separate files for scoring.  For example <a href=../src/sclite/testdata/demo.ref.txt>../src/sclite/testdata/demo.ref.txt</a>
  and <a href=../src/sclite/testdata/demo.hyp.txt>../src/sclite/testdata/demo.hyp.txt</a> are example reference and hypothesis transcripts for 2 utterances.  Since there a two utterances, the transcript for each is labelled with the  utterance id in parens.
<li> Run the scorer with these commands after sclite has been compiled:
  <UL>
    <li> cd src/sclite/testdata
    <li> ../sclite -r demo.ref.txt -h demo.hyp.txt -i spu_id -o sum pra stdout
  </UL>
  <pre>



                     SYSTEM SUMMARY PERCENTAGES by SPEAKER                      

      ,------------------------------------------------------------------.
      |                           demo.hyp.txt                           |
      |------------------------------------------------------------------|
      | SPKR     | # Snt # Wrd | Corr    Sub    Del    Ins    Err  S.Err |
      |----------+-------------+-----------------------------------------|
      | speaker1 |    2     46 | 84.8   15.2    0.0    2.2   17.4   50.0 |
      |==================================================================|
      | Sum/Avg  |    2     46 | 84.8   15.2    0.0    2.2   17.4   50.0 |
      |==================================================================|
      |   Mean   |  2.0   46.0 | 84.8   15.2    0.0    2.2   17.4   50.0 |
      |   S.D.   |  0.0    0.0 |  0.0    0.0    0.0    0.0    0.0    0.0 |
      |  Median  |  2.0   46.0 | 84.8   15.2    0.0    2.2   17.4   50.0 |
      `------------------------------------------------------------------'


		DUMP OF SYSTEM ALIGNMENT STRUCTURE

System name:   demo.hyp.txt

Speakers: 
    0:  speaker1

Speaker sentences   0:  speaker1   #utts: 2
id: (speaker1-utterance1)
Scores: (#C #S #D #I) 25 0 0 0
REF:  as competition in the mutual fund business grows increasingly intense more players in the industry appear willing to sacrifice integrity in the name of performance 
HYP:  as competition in the mutual fund business grows increasingly intense more players in the industry appear willing to sacrifice integrity in the name of performance 
Eval:                                                                                                                                                                     

id: (speaker1-utterance2)
Scores: (#C #S #D #I) 14 7 0 1
REF:  FOR   A  TWO    TRILLION DOLLAR business built on public confidence this trend is **** DISHEARTENING AT  best and downright dangerous at worst 
HYP:  FREED TO TRYING TO       LURE   business built on public confidence this trend is THIS TIGHTENING    AND best and downright dangerous at worst 
Eval: S     S  S      S        S                                                        I    S             S                                         



    
  </pre>

</OL>

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